Modifying Visualizations

Once you’ve created an visualization, it likely needs to be modified to be publication ready. This usually entails creating a title, labelling axis, and resizing the image. This documentation is synthesized from the plotly update_layout documentation.

While somewhat complicated, full documentation for updating figures is available on the plotly website, and should be used as a supplement to this documentation for doing more complex modifications to the figures produced by epidemic-intelligence. Plotly as a whole has excellent documentation and clear examples, and it is highly recommended that you use it if you ever get stuck.

# creating an example plot
import epidemic_intelligence as ei
from google.oauth2 import service_account
from google.cloud import bigquery

credentials = service_account.Credentials.from_service_account_file('../../../credentials.json') # use the path to your credentials
project = 'net-data-viz-handbook' # use your project name
# Initialize a GC client
client = bigquery.Client(credentials=credentials, project=project)

table_name = 'h1n1_R2.basins_prevalence_agg'
reference_table = 'reference.gleam-geo-map'
reference_column = 'basin_id' # name of a column in reference table
geo_column = 'basin_id' # name of a column in table corresponding to column in reference table
geo_level = 'basin_label' 
geo_values = 'Portland(US-ME)' 
value = 'Infectious_18_23'

fig = ei.spaghetti_plot(
    client=client,
    table_name=table_name,
    reference_table=reference_table,
    geo_level=geo_level,
    geo_values=geo_values,
    geo_column=geo_column,
    reference_column=reference_column,
    value=value,
    n=100)

fig

Updating the title, axes, and legend

Full documentation for mofidying the axes can be found here.

# the axis titles can be changed with the xaxis_title and yaxis_title parameters of update_layout. 
fig.update_layout(xaxis_title='Date',
                  yaxis_title='Infectious 18-23')

# the plot title can be set with the title_text keyword.
fig.update_layout(title_text='Sample Spaghetti Plot')

# the legend can be toggled on or off by setting the showlegend keyword to True or False.
fig.update_layout(showlegend=False)
fig

# add ticks 
fig.update_yaxes(ticks="outside")

# add ticks, set the range, and rotate the labels degrees
fig.update_xaxes(ticks="outside", tickangle=60,
                range=[10, 40])
fig

Setting image dimensions

# the plot dimensions, in pixels, can be set using the width and height keywords. 
fig.update_layout(width=800, height=600)

Setting a custom font

Fonts in plotly are tricky, and unfortunately epidemic-intelligence is limited by their limitations. The easiest way to set a custom font is to have it installed on your local system. For example, if you would like to use Barlow Semi Condensed, the simplest path would be to install it through a site like Google Fonts. Note: if a specified font cannot be found, plotly will use Times New Roman.

fig.update_layout(font_family='PT Sans Narrow')

Exporting Visualizations

Full documentation for static image export can be found here.

# also supports pdf, jpg, webp, and svg extensions. 
# the 'scale' parameter multiplies the number of pixels in the image, so scale>1 means higher resolution. 
fig.write_image("fig.png", scale=3)